MCPGod

Overview

MCPGod is a command‑line interface that streamlines the administration of Model Context Protocol (MCP) servers for developers building AI assistants. It tackles a common pain point: keeping track of which servers are active, what tools they expose, and how individual clients (e.g., Claude, GPT‑4) are allowed to interact with those tools. By centralizing these operations in a single, cross‑platform CLI, MCPGod removes the need to juggle configuration files or write custom scripts for each server deployment.

The tool lets you add, list, and remove MCP servers from a client’s configuration with simple commands. You can also selectively expose tools to particular clients, granting fine‑grained permission control. For example, a developer can restrict a production client to only the and tools while allowing a sandbox client full access. This level of control is vital when you need to enforce security or usage policies across multiple AI assistants.

MCPGod also provides direct tool invocation from the terminal. Instead of writing a wrapper or using an external HTTP client, you can call any tool on any server with a single command and pass key‑value arguments. This feature accelerates prototyping, debugging, and automated testing by allowing developers to exercise server functionality without leaving the command line. Additionally, the CLI can run servers locally with verbose logging, automatically storing logs in a structured directory for easy traceability.

Key capabilities include:

• Client‑server mapping: Manage which servers belong to which AI clients.
• Tool discovery: Enumerate all tools available on a server at any time.
• Permission handling: Enable or block specific tools per client, supporting role‑based access control.
• Logging and diagnostics: Capture detailed, timestamped logs for every server run to aid debugging.
• Cross‑platform support: Works on Windows, macOS, and Linux with a single binary.

Typical use cases involve rapid iteration in research labs where new MCP servers are spun up frequently, or production environments that require strict tool access policies. A data scientist can add a new analytics server to their Claude instance, test the tool via the CLI, and then lock down that tool before deploying to production. An operations engineer can audit all active servers for a given client, ensuring compliance with internal security guidelines.

In essence, MCPGod turns the otherwise fragmented task of managing MCP servers into a cohesive workflow. By offering declarative commands for server lifecycle, tool access, and execution—all with transparent logging—it empowers developers to focus on building smarter AI assistants rather than wrestling with infrastructure.